README.md

---
base_model: google-bert/bert-base-uncased
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:91585
- loss:TripletLoss
widget:
- source_sentence: Why do people say "God bless you"?
  sentences:
  - Will the humanity become extinct?
  - Why do people sneeze?
  - Why do they say "God bless you" when you sneeze?
- source_sentence: What clarinet mouthpieces are the best?
  sentences:
  - What is the name of a good web design company in Delhi?
  - Which instrument should I learn?
  - Which clarinet mouthpiece should I buy?
- source_sentence: How do l see who viewed my videos on Instagram?
  sentences:
  - What is the possibility of time travel becoming a reality?
  - Why can't I view a live video I posted on Facebook?
  - How can I see who viewed my video on Instagram but didn't like my video?
- source_sentence: How can I become more social if I am an introvert?
  sentences:
  - What tricks can introverts learn to become more social?
  - Nobody answers my questions on Quora, why?
  - How did you become an introvert?
- source_sentence: How did Halloween Originate? What country did it originate on?
  sentences:
  - What was Halloween like in the 1990s?
  - In what country did Halloween originate?
  - What are the weirdest/creepiest dreams you have ever had?
model-index:
- name: SentenceTransformer based on google-bert/bert-base-uncased
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: QQP nli dev
      type: QQP-nli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.987814465408805
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.012382075471698114
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9874213836477987
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.987814465408805
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.987814465408805
      name: Max Accuracy
---

# SentenceTransformer based on google-bert/bert-base-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("hcy5561/distilroberta-base-sentence-transformer-triplets")
# Run inference
sentences = [
    'How did Halloween Originate? What country did it originate on?',
    'In what country did Halloween originate?',
    'What was Halloween like in the 1990s?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Triplet
* Dataset: `QQP-nli-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.9878     |
| dot_accuracy       | 0.0124     |
| manhattan_accuracy | 0.9874     |
| euclidean_accuracy | 0.9878     |
| **max_accuracy**   | **0.9878** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 91,585 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 13.95 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.02 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.68 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
  | anchor                                                                                | positive                                                                       | negative                                                                                                                         |
  |:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
  | <code>How can I overcome a bad mood?</code>                                           | <code>How do I break out of a bad mood?</code>                                 | <code>The world around me seems so austere and gloomy because of my mood. It's depressing me considerably. What can I do?</code> |
  | <code>What are symptoms of mild schizophrenia?</code>                                 | <code>What are some symptoms of when you become schizophrenic?</code>          | <code>Is confusion another symptom of being schizophrenic?</code>                                                                |
  | <code>What are some ideas which transformed ordinary people into millionaires?</code> | <code>What are some things ordinary people know but millionaires don't?</code> | <code>What can billionaires do that millionaire cannot do?</code>                                                                |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 5,088 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                           |
  | details | <ul><li>min: 6 tokens</li><li>mean: 14.14 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.96 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.8 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
  | anchor                                                                      | positive                                                                | negative                                                                                                                                                                                            |
  |:----------------------------------------------------------------------------|:------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Why do I see the exact same questions in my feed all the time?</code> | <code>Why are too many questions repeating in my feed sometimes?</code> | <code>Why does this "question" keep showing up in the Unorganized Questions global_feed? (see description for screenshot)</code>                                                                    |
  | <code>Can we expect time travel to become a reality?</code>                 | <code>Can we time travel anyhow?</code>                                 | <code>What do you hAve to say about time travel (I am not science student but I read it on net and its so exciting topic but still no clear idea that is it possible or it's just a rumour)?</code> |
  | <code>Is it too late to start medical school at 32?</code>                  | <code>Is it too late to go to medical school at 24?</code>              | <code>As a 14 year old girl who wants to go to medical school, should I work extremely hard and study a lot now to be ready for it? What should I do?</code>                                        |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step  | Training Loss | loss   | QQP-nli-dev_max_accuracy |
|:------:|:-----:|:-------------:|:------:|:------------------------:|
| 0      | 0     | -             | -      | 0.8783                   |
| 0.1746 | 500   | 2.3079        | 0.8664 | 0.9581                   |
| 0.3493 | 1000  | 0.9367        | 0.5027 | 0.9737                   |
| 0.5239 | 1500  | 0.6747        | 0.4471 | 0.9743                   |
| 0.6986 | 2000  | 0.5323        | 0.3740 | 0.9776                   |
| 0.8732 | 2500  | 0.4765        | 0.3178 | 0.9825                   |
| 1.0479 | 3000  | 0.4104        | 0.2809 | 0.9866                   |
| 1.2225 | 3500  | 0.3266        | 0.2633 | 0.9870                   |
| 1.3971 | 4000  | 0.2129        | 0.2566 | 0.9862                   |
| 1.5718 | 4500  | 0.1559        | 0.2542 | 0.9858                   |
| 1.7464 | 5000  | 0.1432        | 0.2482 | 0.9853                   |
| 1.9211 | 5500  | 0.1361        | 0.2370 | 0.9845                   |
| 2.0957 | 6000  | 0.1179        | 0.2102 | 0.9880                   |
| 2.2703 | 6500  | 0.0921        | 0.2201 | 0.9870                   |
| 2.4450 | 7000  | 0.0656        | 0.2075 | 0.9878                   |
| 2.6196 | 7500  | 0.0497        | 0.2011 | 0.9876                   |
| 2.7943 | 8000  | 0.0455        | 0.1960 | 0.9878                   |
| 2.9689 | 8500  | 0.0422        | 0.1973 | 0.9872                   |
| 3.1436 | 9000  | 0.0349        | 0.1863 | 0.9890                   |
| 3.3182 | 9500  | 0.0319        | 0.1850 | 0.9882                   |
| 3.4928 | 10000 | 0.02          | 0.1854 | 0.9882                   |
| 3.6675 | 10500 | 0.0184        | 0.1849 | 0.9884                   |
| 3.8421 | 11000 | 0.0178        | 0.1828 | 0.9878                   |


### Framework Versions
- Python: 3.10.6
- Sentence Transformers: 3.0.1
- Transformers: 4.39.3
- PyTorch: 2.2.2+cu118
- Accelerate: 0.28.0
- Datasets: 2.20.0
- Tokenizers: 0.15.2

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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